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Agent Skill · 研究检索

codex-readiness-unit-test(Codex readiness unit 测试)

用于辅助测试设计、自动化测试、用例整理和回归验证。它适合让 Agent 编写单元测试、端到端测试、测试计划或根据失败日志定位问题。使用时需要确认项目测试框架、运行命令和夹具数据,避免为了通过测试而改坏真实逻辑;涉及浏览器或外部服务时,应区分本地模拟、测试环境和生产环境。

codex-readiness-unit-test 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为接触敏感数据;许可证为 MIT,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 37,042。

总安装 1,812周安装 74GitHub Stars 17,932下载 586核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:codex-readiness-unit-test(Codex readiness unit 测试)
来源仓库:https://github.com/openai/skills
仓库路径:skills/codex-readiness-unit-test
安装命令:
npx skills add https://github.com/openai/skills --skill codex-readiness-unit-test
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/openai/skills --skill codex-readiness-unit-test

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

codex-readiness-unit-test 基于文档质量评估代理准备度,无需外部 API。仅扫描当前目录,禁止 monorepo 发现,输出严格 JSON 格式。支持证据收集与 LLM 内联评估,保持确定性执行流程。需确保技能脚本路径正确且具备文件系统访问权限。

适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/codex-readiness-unit-test

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/openai/skills --skill codex-readiness-unit-tes…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

codex-readiness-unit-test 怎么安装?

在终端执行 npx skills add https://github.com/openai/skills --skill codex-readiness-unit-test。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

codex-readiness-unit-test 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

codex-readiness-unit-test 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

codex-readiness-unit-test 是免费的吗?

来源标注的许可证是 MIT。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

codex-readiness-unit-test 用的人多吗?

公开数据是累计安装 1,812 次,近一周安装 74 次,下载 586 次,GitHub 17,932 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

codex-readiness-unit-test 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 3 个目录同时收录,说明这个 Skill 流传比较广。
  • 许可证明确为 MIT,商用和分发边界好判断。
  • 来源仓库有 17,932 星,维护活跃度相对可查。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
codex-readiness-unit-test

Codex readiness unit 测试 · 本页

1,81217,932接触敏感数据MIT有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

LLM Codex Readiness Unit Test

Instruction-first, in-session "readiness" for evaluating AGENTS/PLANS documentation quality without any external APIs or SDKs. All checks run against the current working directory (cwd), with no monorepo discovery. Each run writes to .codex-readiness-unit-test/<timestamp>/ and updates .codex-readiness-unit-test/latest.json. Keep execution deterministic (filesystem scanning + local command execution only). All LLM evaluation happens in-session and must output strict JSON via the provided references.

Quick Start

  1. Collect evidence:

- python skills/codex-readiness-unit-test/bin/collect_evidence.py

  1. Run deterministic checks:

- python skills/codex-readiness-unit-test/bin/deterministic_rules.py

  1. Run LLM checks using references in references/ and store .codex-readiness-unit-test/<timestamp>/llm_results.json.
  2. If execute mode is requested, build a plan, get confirmation, run:

- python skills/codex-readiness-unit-test/bin/run_plan.py --plan.codex-readiness-unit-test/<timestamp>/plan.json

  1. Generate the report:

- python skills/codex-readiness-unit-test/bin/scoring.py --mode read-only|execute

Outputs (per run, under .codex-readiness-unit-test/<timestamp>/):

  • report.json
  • report.html
  • summary.json
  • logs/* (execute mode)

Runbook

This skill produces a deterministic evidence file plus an in-session LLM evaluation, then compiles a JSON report and HTML scorecard. It requires no OpenAI API key and makes no external HTTP calls.

Minimal Inputs

  • mode: read-only or execute (required)
  • soft_timeout_seconds: optional (default 600)

Modes (Read-only vs Execute)

  • Read-only: Collect evidence, run deterministic rules, and run LLM checks #3–#5. No commands are executed, check #6 is marked NOT_RUN, and no execution logs/summary are produced.
  • Execute: Everything in read-only plus a confirmed plan.json is executed via run_plan.py. This enables check #6 and produces execution logs + execution_summary.json for scoring.

Always ask the user which mode to run (read-only vs. execute) before proceeding.

Check Types

  • Deterministic: filesystem-only checks (#1 AGENTS.md exists, #2 PLANS.md exists, #3 AGENTS.md <= 300 lines, #4 config.toml exists at repo root, repo.codex/, or user.codex/)
  • LLM: in-session Codex evaluation (#3 project context, #4 commands, #5 loops; commands may live in AGENTS or referenced skills)
  • Hybrid: deterministic execution + LLM rationale (#6 execution)

Skill references are discovered from AGENTS.md via $SkillName or .codex/skills/<name> patterns; their SKILL.md files are added to evidence for the LLM checks.

All checks run relative to the current working directory and are defined in skills/codex-readiness-unit-test/references/checks/checks.json, weighted equally by default. Each run writes outputs to .codex-readiness-unit-test/<timestamp>/ and updates .codex-readiness-unit-test/latest.json. The helper scripts read .codex-readiness-unit-test/latest.json by default to locate the latest run directory.

Strict JSON + Retry Loop (Required)

For each LLM/HYBRID check:

  1. Run the specialized prompt expecting strict JSON.
  2. If JSON is invalid or missing keys, run skills/codex-readiness-unit-test/references/json_fix.md with the raw output.
  3. Retry up to 2 additional attempts (max 3 total).
  4. If still invalid: mark the check as WARN with rationale: "Invalid JSON from evaluator after retries".

The JSON schema is:

{
  "status": "PASS|WARN|FAIL|NOT_RUN",
  "rationale": "string",
  "evidence_quotes": [{"path":"...","quote":"..."}],
  "recommendations": ["..."],
  "confidence": 0.0
}

Single Confirmation (Required)

Combine the command summary and execute plan into one concise confirmation step. Present:

  • The extracted build/test/dev loop commands (human-readable, labeled).
  • The planned execute details (cwd, ordered commands, soft timeout policy, env). Ask for a single confirmation to proceed. Do not paste raw JSON, full evidence, or the full plan.json. If declined, mark execute-required checks as NOT_RUN.

Required Files

  • .codex-readiness-unit-test/<timestamp>/evidence.json (from collect_evidence.py)
  • .codex-readiness-unit-test/<timestamp>/deterministic_results.json (from deterministic_rules.py)
  • .codex-readiness-unit-test/<timestamp>/llm_results.json (from in-session references)
  • .codex-readiness-unit-test/<timestamp>/execution_summary.json (execute mode only)
  • .codex-readiness-unit-test/<timestamp>/report.json and .codex-readiness-unit-test/<timestamp>/report.html (from scoring.py)
  • .codex-readiness-unit-test/<timestamp>/summary.json (structured pass/fail summary from scoring.py)
  • .codex-readiness-unit-test/latest.json (stable pointer to the latest run directory)

Prompt Mapping

  • #3 project_context_specified → skills/codex-readiness-unit-test/references/project_context.md
  • #4 build_test_commands_exist → skills/codex-readiness-unit-test/references/commands.md
  • #5 dev_build_test_loops_documented → skills/codex-readiness-unit-test/references/loop_quality.md
  • #6 dev_build_test_loop_execution → skills/codex-readiness-unit-test/references/execution_explanation.md

plan.json schema (execute mode)

{
  "project_dir": "relative/or/absolute/path (optional)",
  "cwd": "optional/absolute/path (defaults to current directory)",
  "commands": [
    {"label": "setup", "cmd": "npm install"},
    {"label": "build", "cmd": "npm run build"},
    {"label": "test", "cmd": "npm test"}
  ],
  "env": {
    "EXAMPLE": "value"
  }
}

Place plan.json inside the run directory (e.g., .codex-readiness-unit-test/<timestamp>/plan.json).

llm_results.json schema

{
  "project_context_specified": {"status":"PASS","rationale":"...","evidence_quotes":[],"recommendations":[],"confidence":0.7},
  "build_test_commands_exist": {"status":"PASS","rationale":"...","evidence_quotes":[],"recommendations":[],"confidence":0.7},
  "dev_build_test_loops_documented": {"status":"WARN","rationale":"...","evidence_quotes":[],"recommendations":[],"confidence":0.6},
  "dev_build_test_loop_execution": {"status":"PASS","rationale":"...","evidence_quotes":[],"recommendations":[],"confidence":0.6}
}

Scoring Rules

  • PASS = 100% of weight
  • WARN = 50% of weight
  • FAIL/NOT_RUN = 0%
  • Overall status: FAIL if any FAIL; else WARN if any WARN or NOT_RUN; else PASS.

Safety + Timeouts

  • Denylisted commands are not executed and marked FAIL.
  • Soft timeout defaults to 600s; hard cap defaults to 3x soft timeout.
  • Execution logs are written to .codex-readiness-unit-test/<timestamp>/logs/.

平台分布

Claude Code

27.74%
按下载量换算163

Codex

22.52%
按下载量换算132

OpenCode

16.2%
按下载量换算95

Gemini CLI

13.76%
按下载量换算81

Antigravity

7.46%
按下载量换算44

Cursor

3.07%
按下载量换算18

安全审计

Gen Agent Trust Hub

可疑

Socket

通过

Snyk

通过

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。

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